Malaria is one of the most devastating infectious diseases in the world. Development of novel antimalarial strategies is urgently needed due to the rapid evolution and spread of drug resistance in malaria parasites Plasmodium. The long term goal of this proposed project is to develop a systems-level understanding of the molecular basis of parasitism, pathogenesis, and drug resistance. We will implement approaches that combine machine learning, probabilistic modeling, and genome-wide association analysis to develop more robust computational solutions and identify a comprehensive set of genes or gene products in biological networks that show an increase in genetic variability that can be associated with drug resistance, pathogenesis, virulence, responses to environmental challenges, or with other interesting phenotypes. The three specific aims are: 1. To identify network components using effective remote homology based methods. We will address a critical barrier in malaria research: our inability to assign functional annotation to over 60% of the predicted gene products in the genome of Plasmodium falciparum. We will use a machine learning approach to detect evolutionarily conserved characteristics of the genes/proteins for network inference. 2. To infer the topology and dynamic interplay of cellular networks. Robust models will be developed to reconstruct the gene regulatory networks, signaling cascades and metabolic pathways that define the genetic basis for disease phenotypes. 3. To identify evolutionary signatures of network models by genome-wide association studies (GWAS). GWAS including Single Nucleotide Polymorphism (SNP) screening of multiple strains with varying phenotypes will serve as an effective means for high throughput wet-lab validations of networks in response to drug treatment. Such networks are the cornerstones of a systems-level view of pathogen biology, a view that will allow us to transform disparate types of data into biological insights for drug development.